In [69]:
!pip install plotly

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In [70]:
!pip install --upgrade plotly
!pip install --upgrade jupyter

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In [97]:
import plotly.express as px
import plotly.graph_objects as go
In [98]:
# Initialize Plotly for Jupyter Notebooks
import plotly.offline as pyo
pyo.init_notebook_mode(connected=True)
In [96]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
In [74]:
matches= pd.read_csv("matches.csv")
delivery= pd.read_csv("deliveries.csv")
In [75]:
matches.head()
Out[75]:
id season city date match_type player_of_match venue team1 team2 toss_winner toss_decision winner result result_margin target_runs target_overs super_over method umpire1 umpire2
0 335982 2007/08 Bangalore 2008-04-18 League BB McCullum M Chinnaswamy Stadium Royal Challengers Bangalore Kolkata Knight Riders Royal Challengers Bangalore field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
1 335983 2007/08 Chandigarh 2008-04-19 League MEK Hussey Punjab Cricket Association Stadium, Mohali Kings XI Punjab Chennai Super Kings Chennai Super Kings bat Chennai Super Kings runs 33.0 241.0 20.0 N NaN MR Benson SL Shastri
2 335984 2007/08 Delhi 2008-04-19 League MF Maharoof Feroz Shah Kotla Delhi Daredevils Rajasthan Royals Rajasthan Royals bat Delhi Daredevils wickets 9.0 130.0 20.0 N NaN Aleem Dar GA Pratapkumar
3 335985 2007/08 Mumbai 2008-04-20 League MV Boucher Wankhede Stadium Mumbai Indians Royal Challengers Bangalore Mumbai Indians bat Royal Challengers Bangalore wickets 5.0 166.0 20.0 N NaN SJ Davis DJ Harper
4 335986 2007/08 Kolkata 2008-04-20 League DJ Hussey Eden Gardens Kolkata Knight Riders Deccan Chargers Deccan Chargers bat Kolkata Knight Riders wickets 5.0 111.0 20.0 N NaN BF Bowden K Hariharan
In [76]:
delivery.head()
Out[76]:
match_id inning batting_team bowling_team over ball batter bowler non_striker batsman_runs extra_runs total_runs extras_type is_wicket player_dismissed dismissal_kind fielder
0 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 1 SC Ganguly P Kumar BB McCullum 0 1 1 legbyes 0 NaN NaN NaN
1 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 2 BB McCullum P Kumar SC Ganguly 0 0 0 NaN 0 NaN NaN NaN
2 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 3 BB McCullum P Kumar SC Ganguly 0 1 1 wides 0 NaN NaN NaN
3 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 4 BB McCullum P Kumar SC Ganguly 0 0 0 NaN 0 NaN NaN NaN
4 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 5 BB McCullum P Kumar SC Ganguly 0 0 0 NaN 0 NaN NaN NaN
In [77]:
matches.tail()
Out[77]:
id season city date match_type player_of_match venue team1 team2 toss_winner toss_decision winner result result_margin target_runs target_overs super_over method umpire1 umpire2
1090 1426307 2024 Hyderabad 2024-05-19 League Abhishek Sharma Rajiv Gandhi International Stadium, Uppal, Hyd... Punjab Kings Sunrisers Hyderabad Punjab Kings bat Sunrisers Hyderabad wickets 4.0 215.0 20.0 N NaN Nitin Menon VK Sharma
1091 1426309 2024 Ahmedabad 2024-05-21 Qualifier 1 MA Starc Narendra Modi Stadium, Ahmedabad Sunrisers Hyderabad Kolkata Knight Riders Sunrisers Hyderabad bat Kolkata Knight Riders wickets 8.0 160.0 20.0 N NaN AK Chaudhary R Pandit
1092 1426310 2024 Ahmedabad 2024-05-22 Eliminator R Ashwin Narendra Modi Stadium, Ahmedabad Royal Challengers Bengaluru Rajasthan Royals Rajasthan Royals field Rajasthan Royals wickets 4.0 173.0 20.0 N NaN KN Ananthapadmanabhan MV Saidharshan Kumar
1093 1426311 2024 Chennai 2024-05-24 Qualifier 2 Shahbaz Ahmed MA Chidambaram Stadium, Chepauk, Chennai Sunrisers Hyderabad Rajasthan Royals Rajasthan Royals field Sunrisers Hyderabad runs 36.0 176.0 20.0 N NaN Nitin Menon VK Sharma
1094 1426312 2024 Chennai 2024-05-26 Final MA Starc MA Chidambaram Stadium, Chepauk, Chennai Sunrisers Hyderabad Kolkata Knight Riders Sunrisers Hyderabad bat Kolkata Knight Riders wickets 8.0 114.0 20.0 N NaN J Madanagopal Nitin Menon
In [78]:
delivery.tail()
Out[78]:
match_id inning batting_team bowling_team over ball batter bowler non_striker batsman_runs extra_runs total_runs extras_type is_wicket player_dismissed dismissal_kind fielder
260915 1426312 2 Kolkata Knight Riders Sunrisers Hyderabad 9 5 SS Iyer AK Markram VR Iyer 1 0 1 NaN 0 NaN NaN NaN
260916 1426312 2 Kolkata Knight Riders Sunrisers Hyderabad 9 6 VR Iyer AK Markram SS Iyer 1 0 1 NaN 0 NaN NaN NaN
260917 1426312 2 Kolkata Knight Riders Sunrisers Hyderabad 10 1 VR Iyer Shahbaz Ahmed SS Iyer 1 0 1 NaN 0 NaN NaN NaN
260918 1426312 2 Kolkata Knight Riders Sunrisers Hyderabad 10 2 SS Iyer Shahbaz Ahmed VR Iyer 1 0 1 NaN 0 NaN NaN NaN
260919 1426312 2 Kolkata Knight Riders Sunrisers Hyderabad 10 3 VR Iyer Shahbaz Ahmed SS Iyer 1 0 1 NaN 0 NaN NaN NaN
In [79]:
ipl= delivery.merge(matches, left_on= 'match_id', right_on ='id')
ipl.head()
Out[79]:
match_id inning batting_team bowling_team over ball batter bowler non_striker batsman_runs ... toss_decision winner result result_margin target_runs target_overs super_over method umpire1 umpire2
0 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 1 SC Ganguly P Kumar BB McCullum 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
1 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 2 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
2 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 3 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
3 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 4 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
4 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 5 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen

5 rows × 37 columns

avg vs sr graph for top 50 batsman(in trumns of run)¶

fetching a new dataframe with top 50 batsman¶

In [80]:
top50 = ipl.groupby('batter')['batsman_runs'].sum().sort_values(ascending=False).head(50).index
In [81]:
top50
Out[81]:
Index(['V Kohli', 'S Dhawan', 'RG Sharma', 'DA Warner', 'SK Raina', 'MS Dhoni',
       'AB de Villiers', 'CH Gayle', 'RV Uthappa', 'KD Karthik', 'KL Rahul',
       'AM Rahane', 'F du Plessis', 'SV Samson', 'AT Rayudu', 'G Gambhir',
       'SR Watson', 'MK Pandey', 'SA Yadav', 'JC Buttler', 'KA Pollard',
       'RR Pant', 'YK Pathan', 'Shubman Gill', 'Q de Kock', 'SS Iyer',
       'RA Jadeja', 'WP Saha', 'DA Miller', 'BB McCullum', 'PA Patel',
       'GJ Maxwell', 'Yuvraj Singh', 'V Sehwag', 'MA Agarwal', 'Ishan Kishan',
       'N Rana', 'M Vijay', 'HH Pandya', 'SPD Smith', 'SE Marsh', 'AD Russell',
       'JH Kallis', 'DR Smith', 'RD Gaikwad', 'SR Tendulkar', 'RA Tripathi',
       'R Dravid', 'KS Williamson', 'AJ Finch'],
      dtype='object', name='batter')
In [82]:
new_ipl= ipl[ipl['batter'].isin(top50)]
In [83]:
new_ipl
Out[83]:
match_id inning batting_team bowling_team over ball batter bowler non_striker batsman_runs ... toss_decision winner result result_margin target_runs target_overs super_over method umpire1 umpire2
1 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 2 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
2 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 3 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
3 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 4 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
4 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 5 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
5 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 6 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
260763 1426312 1 Sunrisers Hyderabad Kolkata Knight Riders 4 1 RA Tripathi MA Starc AK Markram 0 ... bat Kolkata Knight Riders wickets 8.0 114.0 20.0 N NaN J Madanagopal Nitin Menon
260764 1426312 1 Sunrisers Hyderabad Kolkata Knight Riders 4 2 RA Tripathi MA Starc AK Markram 0 ... bat Kolkata Knight Riders wickets 8.0 114.0 20.0 N NaN J Madanagopal Nitin Menon
260910 1426312 2 Kolkata Knight Riders Sunrisers Hyderabad 8 6 SS Iyer Shahbaz Ahmed VR Iyer 4 ... bat Kolkata Knight Riders wickets 8.0 114.0 20.0 N NaN J Madanagopal Nitin Menon
260915 1426312 2 Kolkata Knight Riders Sunrisers Hyderabad 9 5 SS Iyer AK Markram VR Iyer 1 ... bat Kolkata Knight Riders wickets 8.0 114.0 20.0 N NaN J Madanagopal Nitin Menon
260918 1426312 2 Kolkata Knight Riders Sunrisers Hyderabad 10 2 SS Iyer Shahbaz Ahmed VR Iyer 1 ... bat Kolkata Knight Riders wickets 8.0 114.0 20.0 N NaN J Madanagopal Nitin Menon

139298 rows × 37 columns

calculating SR¶

sr= (number of runs scored)/(number of balls played)*100¶

In [84]:
runs= new_ipl.groupby('batter')['batsman_runs'].sum()
balls= new_ipl.groupby('batter')['batsman_runs'].count()

sr= (runs/balls)*100
sr=sr.reset_index()
In [85]:
sr
Out[85]:
batter batsman_runs
0 AB de Villiers 148.580442
1 AD Russell 164.224422
2 AJ Finch 123.349057
3 AM Rahane 120.321410
4 AT Rayudu 124.584527
5 BB McCullum 126.848592
6 CH Gayle 142.121729
7 DA Miller 134.684477
8 DA Warner 135.429986
9 DR Smith 132.279534
10 F du Plessis 133.071325
11 G Gambhir 119.665153
12 GJ Maxwell 150.488599
13 HH Pandya 139.691290
14 Ishan Kishan 132.797589
15 JC Buttler 142.238984
16 JH Kallis 105.936272
17 KA Pollard 140.457703
18 KD Karthik 131.353404
19 KL Rahul 131.050866
20 KS Williamson 122.952710
21 M Vijay 118.614130
22 MA Agarwal 128.255646
23 MK Pandey 117.366180
24 MS Dhoni 132.835065
25 N Rana 130.818859
26 PA Patel 116.625717
27 Q de Kock 131.120332
28 R Dravid 113.347237
29 RA Jadeja 124.432296
30 RA Tripathi 135.515152
31 RD Gaikwad 133.632791
32 RG Sharma 127.918194
33 RR Pant 143.597561
34 RV Uthappa 126.152279
35 S Dhawan 123.454313
36 SA Yadav 142.505948
37 SE Marsh 130.109775
38 SK Raina 132.535312
39 SPD Smith 124.812406
40 SR Tendulkar 114.187867
41 SR Watson 134.163209
42 SS Iyer 123.025540
43 SV Samson 135.137615
44 Shubman Gill 132.236842
45 V Kohli 128.511867
46 V Sehwag 148.827059
47 WP Saha 123.902027
48 YK Pathan 138.046272
49 Yuvraj Singh 124.784776

calculated avg¶

avg= (total number of runs)/(total numbers of out)¶

In [86]:
out= ipl[ipl['player_dismissed'].isin(top50)]

nout= out['player_dismissed'].value_counts()

avg= runs/nout
avg=avg.reset_index()

avg.rename(columns={'index': 'batter', 0: 'avg'}, inplace= True)

avg=avg.merge(sr, on= 'batter')
In [87]:
avg
Out[87]:
batter avg batsman_runs
0 AB de Villiers 39.853846 148.580442
1 AD Russell 28.930233 164.224422
2 AJ Finch 24.904762 123.349057
3 AM Rahane 30.142857 120.321410
4 AT Rayudu 28.051613 124.584527
5 BB McCullum 27.711538 126.848592
6 CH Gayle 39.658730 142.121729
7 DA Miller 35.658537 134.684477
8 DA Warner 40.042683 135.429986
9 DR Smith 28.392857 132.279534
10 F du Plessis 35.992126 133.071325
11 G Gambhir 31.007353 119.665153
12 GJ Maxwell 24.750000 150.488599
13 HH Pandya 28.471910 139.691290
14 Ishan Kishan 28.430108 132.797589
15 JC Buttler 37.715789 142.238984
16 JH Kallis 28.552941 105.936272
17 KA Pollard 28.404959 140.457703
18 KD Karthik 26.320652 131.353404
19 KL Rahul 44.657143 131.050866
20 KS Williamson 35.533333 122.952710
21 M Vijay 25.930693 118.614130
22 MA Agarwal 22.811966 128.255646
23 MK Pandey 29.015038 117.366180
24 MS Dhoni 39.126866 132.835065
25 N Rana 28.344086 130.818859
26 PA Patel 22.603175 116.625717
27 Q de Kock 30.980392 131.120332
28 R Dravid 28.233766 113.347237
29 RA Jadeja 27.398148 124.432296
30 RA Tripathi 26.939759 135.515152
31 RD Gaikwad 41.754386 133.632791
32 RG Sharma 29.730942 127.918194
33 RR Pant 35.451613 143.597561
34 RV Uthappa 27.522222 126.152279
35 S Dhawan 35.072539 123.454313
36 SA Yadav 31.805310 142.505948
37 SE Marsh 39.507937 130.109775
38 SK Raina 32.374269 132.535312
39 SPD Smith 34.652778 124.812406
40 SR Tendulkar 33.826087 114.187867
41 SR Watson 30.793651 134.163209
42 SS Iyer 31.948980 123.025540
43 SV Samson 30.687500 135.137615
44 Shubman Gill 37.835294 132.236842
45 V Kohli 38.714976 128.511867
46 V Sehwag 27.555556 148.827059
47 WP Saha 24.247934 123.902027
48 YK Pathan 29.290909 138.046272
49 Yuvraj Singh 24.810811 124.784776
In [88]:
nout
Out[88]:
RG Sharma         223
V Kohli           207
S Dhawan          193
KD Karthik        184
RV Uthappa        180
SK Raina          171
DA Warner         164
AT Rayudu         155
AM Rahane         154
SV Samson         144
G Gambhir         136
MS Dhoni          134
MK Pandey         133
AB de Villiers    130
F du Plessis      127
PA Patel          126
SR Watson         126
CH Gayle          126
WP Saha           121
KA Pollard        121
MA Agarwal        117
SA Yadav          113
GJ Maxwell        112
Yuvraj Singh      111
YK Pathan         110
RA Jadeja         108
KL Rahul          105
BB McCullum       104
Q de Kock         102
M Vijay           101
V Sehwag           99
SS Iyer            98
JC Buttler         95
N Rana             93
Ishan Kishan       93
RR Pant            93
HH Pandya          89
AD Russell         86
Shubman Gill       85
JH Kallis          85
AJ Finch           84
DR Smith           84
RA Tripathi        83
DA Miller          82
R Dravid           77
SPD Smith          72
SR Tendulkar       69
SE Marsh           63
KS Williamson      60
RD Gaikwad         57
Name: player_dismissed, dtype: int64

Scatter plot¶

In [89]:
import plotly.graph_objs as go
import plotly.offline as pyo
In [90]:
# Create scatter plot trace
trace_plot = go.Scatter(x=avg['avg'], y=avg['batsman_runs'], mode='markers')

# Data for the plot
data = [trace_plot]

# Define layout for the plot
layout = go.Layout(title='Batsman Avg vs SR',
                   xaxis={'title': 'Batsman Average'},
                   yaxis={'title': 'Strike Rate'})

# Create figure
fig = go.Figure(data=data, layout=layout)


pyo.plot(fig)
Out[90]:
'temp-plot.html'
In [91]:
trace_plot = go.Scatter(
    x=avg['avg'], 
    y=avg['batsman_runs'], 
    mode='markers',
    marker=dict(
        size=12,                # Adjust size of markers
        color=avg['batsman_runs'],  # Color based on runs
        colorscale='Viridis',    # Apply color scale
        showscale=True           # Show color scale bar
    )
)
In [92]:
layout = go.Layout(title='Batsman Avg vs SR',
                   xaxis={'title': 'Batsman Average'},
                   yaxis={'title': 'Strike Rate'})

# Create figure
fig = go.Figure(data=data, layout=layout)


pyo.plot(fig)
Out[92]:
'temp-plot.html'
In [93]:
trace_plot = go.Scatter(
    x=avg['avg'], 
    y=avg['batsman_runs'], 
    mode='markers',
    marker=dict(size=12, color=avg['batsman_runs'], colorscale='Viridis', showscale=True),
    text=avg['batter'],  # Show player name on hover
    hoverinfo='text+x+y'      # Display both x and y values along with text
)
In [94]:
layout = go.Layout(title='Batsman Avg vs SR',
                   xaxis={'title': 'Batsman Average'},
                   yaxis={'title': 'Strike Rate'})

# Create figure
fig = go.Figure(data=data, layout=layout)


pyo.plot(fig)
Out[94]:
'temp-plot.html'
In [95]:
pyo.iplot(fig)
In [96]:
annotations = [
    dict(
        x=avg['avg'][idx], 
        y=avg['batsman_runs'][idx], 
        text=f"Top Player: {avg['batter'][idx]}",
        showarrow=True,
        arrowhead=2,
        ax=20,
        ay=-30
    ) for idx in [0, 1, 2]  # Highlight first three top players
]
layout = go.Layout(
    title='Batsman Avg vs SR',
    xaxis={'title': 'Batsman Average'},
    yaxis={'title': 'Strike Rate'},
    annotations=annotations   # Add annotations to layout
)
In [102]:
# Create figure
fig = go.Figure(data=data, layout=layout)


pyo.plot(fig)
Out[102]:
'temp-plot.html'
In [103]:
pyo.iplot(fig)
In [99]:
layout = go.Layout(
    title='Batsman Avg vs SR',
    xaxis=dict(
        title='Batsman Average',
        rangeselector=dict(
            buttons=list([
                dict(count=1, label="1m", step="month", stepmode="backward"),
                dict(count=6, label="6m", step="month", stepmode="backward"),
                dict(step="all")
            ])
        ),
        rangeslider=dict(visible=True),  # Add a range slider
    )
)
In [100]:
# Create figure
fig = go.Figure(data=data, layout=layout)


pyo.plot(fig)
Out[100]:
'temp-plot.html'
In [101]:
pyo.iplot(fig)

year by performance¶

In [102]:
single= ipl[ipl['batter']== 'V Kohli']
performance= single.groupby('season')['batsman_runs'].sum().reset_index()
performance
Out[102]:
season batsman_runs
0 2007/08 165
1 2009 246
2 2009/10 307
3 2011 557
4 2012 364
5 2013 639
6 2014 359
7 2015 505
8 2016 973
9 2017 308
10 2018 530
11 2019 464
12 2020/21 471
13 2021 405
14 2022 341
15 2023 639
16 2024 741
In [103]:
single= ipl[ipl['batter']== 'F du Plessis']
performance1= single.groupby('season')['batsman_runs'].sum().reset_index()
performance1
Out[103]:
season batsman_runs
0 2012 398
1 2014 303
2 2015 380
3 2016 206
4 2017 8
5 2018 162
6 2019 396
7 2020/21 449
8 2021 633
9 2022 468
10 2023 730
11 2024 438

PLOT LINE CHART¶

In [104]:
trace = go.Scatter(
    x=performance['season'],
    y=performance['batsman_runs'],
    mode='lines+markers',
    marker=dict(color='#00a65a')
)

data=[trace]

layout = go.Layout(
    title='Year by Performance',
    xaxis={'title': 'Season'},
    yaxis={'title': 'Batsman Runs'}
)

# Create figure
fig1 = go.Figure(data=data, layout=layout)
In [105]:
# Plot the figure
pyo.iplot(fig1)  # Use this for Jupyter Notebooks


# or
pyo.plot(fig1)  # Use this for a standalone HTML file
Out[105]:
'temp-plot.html'
In [106]:
performance = {
    'season': [2018, 2019, 2020, 2021, 2022],
    'batsman_runs': [500, 600, 400, 700, 800]
}
In [107]:
# Sample performance data (replace this with your actual DataFrame)
performance = {
    'season': [2018, 2019, 2020, 2021, 2022],
    'batsman_runs': [500, 600, 400, 700, 800]
}

trace = go.Scatter(
    x=performance['season'],
    y=performance['batsman_runs'],
    mode='lines+markers', 
    marker=dict(color='#00a65a')
)

data = [trace]

layout = go.Layout(
    title='Year by Performance',
    xaxis=dict(title='Season'),
    yaxis=dict(title='Batsman Runs')
)

# Create figure
fig = go.Figure(data=data, layout=layout)


# Plot the figure
pyo.iplot(fig)  # Use this for Jupyter Notebooks


# or
pyo.plot(fig)  # Use this for a standalone HTML file
Out[107]:
'temp-plot.html'

multi line chart¶

In [108]:
trace = go.Scatter(
    x=performance['season'],
    y=performance['batsman_runs'],
    mode='lines+markers',
    marker=dict(color='#00a65a')
)

trace1 = go.Scatter(
    x=performance1['season'],
    y=performance1['batsman_runs'],
    mode='lines+markers'
)

# Put both traces in the data list
data = [trace, trace1]

layout = go.Layout(
    title='Year by Performance',
    xaxis={'title': 'Season'},
    yaxis={'title': 'Batsman Runs'}
)

# Create figure
fig2 = go.Figure(data=data, layout=layout)
In [109]:
# Plot the figure
pyo.iplot(fig2)  # Use this for Jupyter Notebooks


# or
pyo.plot(fig2)  # Use this for a standalone HTML file
Out[109]:
'temp-plot.html'

bar plot¶

In [110]:
top10 = ipl.groupby('batter')['batsman_runs'].sum().sort_values(ascending=False).head(50).index
top10_df=ipl[ipl['batter'].isin(top10)]
In [111]:
top10_df
Out[111]:
match_id inning batting_team bowling_team over ball batter bowler non_striker batsman_runs ... toss_decision winner result result_margin target_runs target_overs super_over method umpire1 umpire2
1 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 2 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
2 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 3 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
3 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 4 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
4 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 5 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
5 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 0 6 BB McCullum P Kumar SC Ganguly 0 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
260763 1426312 1 Sunrisers Hyderabad Kolkata Knight Riders 4 1 RA Tripathi MA Starc AK Markram 0 ... bat Kolkata Knight Riders wickets 8.0 114.0 20.0 N NaN J Madanagopal Nitin Menon
260764 1426312 1 Sunrisers Hyderabad Kolkata Knight Riders 4 2 RA Tripathi MA Starc AK Markram 0 ... bat Kolkata Knight Riders wickets 8.0 114.0 20.0 N NaN J Madanagopal Nitin Menon
260910 1426312 2 Kolkata Knight Riders Sunrisers Hyderabad 8 6 SS Iyer Shahbaz Ahmed VR Iyer 4 ... bat Kolkata Knight Riders wickets 8.0 114.0 20.0 N NaN J Madanagopal Nitin Menon
260915 1426312 2 Kolkata Knight Riders Sunrisers Hyderabad 9 5 SS Iyer AK Markram VR Iyer 1 ... bat Kolkata Knight Riders wickets 8.0 114.0 20.0 N NaN J Madanagopal Nitin Menon
260918 1426312 2 Kolkata Knight Riders Sunrisers Hyderabad 10 2 SS Iyer Shahbaz Ahmed VR Iyer 1 ... bat Kolkata Knight Riders wickets 8.0 114.0 20.0 N NaN J Madanagopal Nitin Menon

139298 rows × 37 columns

In [113]:
import plotly.graph_objects as go

trace = go.Bar(
    x=top10_df['batter'],
    y=top10_df['batsman_runs']
)

data = [trace]

layout = go.Layout(
    title='Top 10 IPL Batsmen',
    xaxis={'title': 'Batsman Name'},
    yaxis={'title': 'Batsman Runs'}
)

fig3 = go.Figure(data=data, layout=layout)
In [ ]:
# Plot the figure
pyo.iplot(fig3)  # Use this for Jupyter Notebooks


# or
pyo.plot(fig3)

Add Colors Based on Values¶

In [114]:
trace = go.Bar(
    x=top10_df['batter'],
    y=top10_df['batsman_runs'],
    marker=dict(
        color=top10_df['batsman_runs'],  # Color based on the number of runs
        colorscale='Viridis', 
        showscale=True  # Display color scale bar
    )
)

Add Annotations to Highlight Key Performers¶

In [46]:
annotations = [
    dict(
        x='Virat Kohli',  # Input  Batsman's name
        y=973,  # Runs scored
        xref='x',
        yref='y',
        text='Highest Scorer',  # Annotation text
        showarrow=True,
        arrowhead=7,
        ax=0,
        ay=-40
    )
]

layout = go.Layout(
    title='Top 10 IPL Batsmen by Runs',
    xaxis={'title': 'Batsman Name'},
    yaxis={'title': 'Batsman Runs'},
    annotations=annotations  # Add the annotations
)
In [47]:
fig6 = go.Figure(data=data, layout=layout)
In [48]:
pyo.plot(fig6)
Out[48]:
'temp-plot.html'

Add a Range Slider to Zoom into Specific Data¶

In [98]:
layout = go.Layout(
    title='Top 10 IPL Batsmen by Runs',
    xaxis=dict(
        title='Batsman Name',
        rangeslider=dict(visible=True),  # Add range slider for x-axis
        type='category'  # Set type as category for non-numeric x-axis
    ),
    yaxis={'title': 'Batsman Runs'}
)

Stacked Bar Chart to Show Multiple Metrics¶

In [99]:
trace1 = go.Bar(
    x=top10_df['batter'],
    y=top10_df['batsman_runs'],
    name='Runs',
    marker=dict(color='blue')
)

trace2 = go.Bar(
    x=top10_df['batter'],
    y=top10_df['ball'],
    name='ball',
    marker=dict(color='green')
)

data = [trace1, trace2]

layout = go.Layout(
    title='Top 10 IPL Batsmen: Runs vs Strike Rate',
    xaxis={'title': 'Batsman Name'},
    yaxis={'title': 'Metrics'},
    barmode='stack'  # Stack the bars
)
In [100]:
fig7 = go.Figure(data=data, layout=layout)
In [101]:
pyo.plot(fig7)
Out[101]:
'temp-plot.html'

Grouped Bar Chart for Comparison¶

In [102]:
trace1 = go.Bar(
    x=top10_df['batter'],
    y=top10_df['batsman_runs'],
    name='2021 Season',
    marker=dict(color='blue')
)

trace2 = go.Bar(
    x=top10_df['batter'],
    y=top10_df['batsman_runs'],
    name='2022 Season',
    marker=dict(color='green')
)

data = [trace1, trace2]

layout = go.Layout(
    title='Top 10 IPL Batsmen by Season',
    xaxis={'title': 'Batsman Name'},
    yaxis={'title': 'Batsman Runs'},
    barmode='group'  # Grouped bars
)
In [103]:
fig8 = go.Figure(data=data, layout=layout)
In [104]:
pyo.plot(fig8)
Out[104]:
'temp-plot.html'

BUBBLE CHART¶

In [105]:
new_ipl= new_ipl[new_ipl['batsman_runs']== 6]
In [106]:
new_ipl
Out[106]:
match_id inning batting_team bowling_team over ball batter bowler non_striker batsman_runs ... toss_decision winner result result_margin target_runs target_overs super_over method umpire1 umpire2
10 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 1 4 BB McCullum Z Khan SC Ganguly 6 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
20 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 3 2 BB McCullum AA Noffke SC Ganguly 6 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
25 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 3 7 BB McCullum AA Noffke SC Ganguly 6 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
60 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 9 5 BB McCullum SB Joshi RT Ponting 6 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
69 335982 1 Kolkata Knight Riders Royal Challengers Bangalore 11 2 BB McCullum SB Joshi RT Ponting 6 ... field Kolkata Knight Riders runs 140.0 223.0 20.0 N NaN Asad Rauf RE Koertzen
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
260254 1426310 1 Royal Challengers Bengaluru Rajasthan Royals 1 5 F du Plessis Sandeep Sharma V Kohli 6 ... field Rajasthan Royals wickets 4.0 173.0 20.0 N NaN KN Ananthapadmanabhan MV Saidharshan Kumar
260262 1426310 1 Royal Challengers Bengaluru Rajasthan Royals 3 1 V Kohli Avesh Khan F du Plessis 6 ... field Rajasthan Royals wickets 4.0 173.0 20.0 N NaN KN Ananthapadmanabhan MV Saidharshan Kumar
260402 1426310 2 Rajasthan Royals Royal Challengers Bengaluru 6 1 SV Samson Swapnil Singh YBK Jaiswal 6 ... field Rajasthan Royals wickets 4.0 173.0 20.0 N NaN KN Ananthapadmanabhan MV Saidharshan Kumar
260506 1426311 1 Sunrisers Hyderabad Rajasthan Royals 3 4 RA Tripathi R Ashwin TM Head 6 ... field Sunrisers Hyderabad runs 36.0 176.0 20.0 N NaN Nitin Menon VK Sharma
260509 1426311 1 Sunrisers Hyderabad Rajasthan Royals 4 1 RA Tripathi TA Boult TM Head 6 ... field Sunrisers Hyderabad runs 36.0 176.0 20.0 N NaN Nitin Menon VK Sharma

7091 rows × 37 columns

In [107]:
six= new_ipl.groupby('batter')['batsman_runs'].count().reset_index()
In [108]:
six
Out[108]:
batter batsman_runs
0 AB de Villiers 253
1 AD Russell 209
2 AJ Finch 78
3 AM Rahane 103
4 AT Rayudu 173
5 BB McCullum 130
6 CH Gayle 359
7 DA Miller 134
8 DA Warner 236
9 DR Smith 117
10 F du Plessis 166
11 G Gambhir 59
12 GJ Maxwell 160
13 HH Pandya 137
14 Ishan Kishan 119
15 JC Buttler 161
16 JH Kallis 44
17 KA Pollard 224
18 KD Karthik 161
19 KL Rahul 187
20 KS Williamson 64
21 M Vijay 91
22 MA Agarwal 98
23 MK Pandey 111
24 MS Dhoni 252
25 N Rana 132
26 PA Patel 49
27 Q de Kock 123
28 R Dravid 28
29 RA Jadeja 107
30 RA Tripathi 84
31 RD Gaikwad 91
32 RG Sharma 281
33 RR Pant 154
34 RV Uthappa 182
35 S Dhawan 153
36 SA Yadav 130
37 SE Marsh 78
38 SK Raina 204
39 SPD Smith 60
40 SR Tendulkar 29
41 SR Watson 190
42 SS Iyer 113
43 SV Samson 206
44 Shubman Gill 95
45 V Kohli 273
46 V Sehwag 106
47 WP Saha 87
48 YK Pathan 161
49 Yuvraj Singh 149
In [109]:
x=avg.merge(six, on= 'batter')
x
Out[109]:
batter avg batsman_runs_x batsman_runs_y
0 AB de Villiers 39.853846 148.580442 253
1 AD Russell 28.930233 164.224422 209
2 AJ Finch 24.904762 123.349057 78
3 AM Rahane 30.142857 120.321410 103
4 AT Rayudu 28.051613 124.584527 173
5 BB McCullum 27.711538 126.848592 130
6 CH Gayle 39.658730 142.121729 359
7 DA Miller 35.658537 134.684477 134
8 DA Warner 40.042683 135.429986 236
9 DR Smith 28.392857 132.279534 117
10 F du Plessis 35.992126 133.071325 166
11 G Gambhir 31.007353 119.665153 59
12 GJ Maxwell 24.750000 150.488599 160
13 HH Pandya 28.471910 139.691290 137
14 Ishan Kishan 28.430108 132.797589 119
15 JC Buttler 37.715789 142.238984 161
16 JH Kallis 28.552941 105.936272 44
17 KA Pollard 28.404959 140.457703 224
18 KD Karthik 26.320652 131.353404 161
19 KL Rahul 44.657143 131.050866 187
20 KS Williamson 35.533333 122.952710 64
21 M Vijay 25.930693 118.614130 91
22 MA Agarwal 22.811966 128.255646 98
23 MK Pandey 29.015038 117.366180 111
24 MS Dhoni 39.126866 132.835065 252
25 N Rana 28.344086 130.818859 132
26 PA Patel 22.603175 116.625717 49
27 Q de Kock 30.980392 131.120332 123
28 R Dravid 28.233766 113.347237 28
29 RA Jadeja 27.398148 124.432296 107
30 RA Tripathi 26.939759 135.515152 84
31 RD Gaikwad 41.754386 133.632791 91
32 RG Sharma 29.730942 127.918194 281
33 RR Pant 35.451613 143.597561 154
34 RV Uthappa 27.522222 126.152279 182
35 S Dhawan 35.072539 123.454313 153
36 SA Yadav 31.805310 142.505948 130
37 SE Marsh 39.507937 130.109775 78
38 SK Raina 32.374269 132.535312 204
39 SPD Smith 34.652778 124.812406 60
40 SR Tendulkar 33.826087 114.187867 29
41 SR Watson 30.793651 134.163209 190
42 SS Iyer 31.948980 123.025540 113
43 SV Samson 30.687500 135.137615 206
44 Shubman Gill 37.835294 132.236842 95
45 V Kohli 38.714976 128.511867 273
46 V Sehwag 27.555556 148.827059 106
47 WP Saha 24.247934 123.902027 87
48 YK Pathan 29.290909 138.046272 161
49 Yuvraj Singh 24.810811 124.784776 149
In [110]:
import plotly.graph_objects as go
import plotly.offline as pyo
pyo.init_notebook_mode(connected=True)

# Create a bubble chart
trace = go.Scatter(
    x=x['avg'],
    y=x['batsman_runs_x'],
    mode='markers',  # Display markers
    marker=dict(
        size=x['batsman_runs_y'],  # Size of markers
        color=x['batsman_runs_x'],  # Color by another dimension (optional)
        showscale=True  # Show color scale
    )
)

# Create layout
layout = go.Layout(
    title='Bubble Chart for IPL Dataset',
    xaxis={'title': 'Average'},
    yaxis={'title': 'SR'}
)

# Create figure
fig14 = go.Figure(data=[trace], layout=layout)
In [111]:
fig14.show()
In [112]:
pyo.plot(fig14)
Out[112]:
'temp-plot.html'
In [112]:
import plotly.graph_objects as go
import plotly.offline as pyo
pyo.init_notebook_mode(connected=True)

# Create a bubble chart using the 'batter' names and 'batsman_runs'
trace = go.Scatter(
    x=top10_df['batter'],
    y=top10_df['batsman_runs'],
    mode='markers',
    marker=dict(
        size=top10_df['batsman_runs'] / 10, 
        color=top10_df['batsman_runs'],    
        showscale=True
    )
)

layout = go.Layout(
    title='Top 10 IPL Batsmen - Bubble Chart',
    xaxis={'title': 'Batsman Name'},
    yaxis={'title': 'Batsman Runs'},
    hovermode='closest'
)

fig11 = go.Figure(data=[trace], layout=layout)
In [75]:
pyo.plot(fig11)
Out[75]:
'temp-plot.html'

HEATMAP¶

In [113]:
import plotly.graph_objects as go

# Create a heatmap (assuming you have more data, like batsman names and multiple seasons)
trace = go.Heatmap(
    z=top10_df['batsman_runs'],  # Data to color by
    x=top10_df['batter'],    
    y=top10_df['season'],        # Seasons (if available in dataset)
    colorscale='Viridis'         # Choose a color scale
)

layout = go.Layout(
    title='Batsman Runs Heatmap',
    xaxis={'title': 'Batsman'},
    yaxis={'title': 'Season'},
)

fig12 = go.Figure(data=[trace], layout=layout)
In [114]:
fig12.show()
In [117]:
pyo.plot(fig12)
Out[117]:
'temp-plot.html'

TREEMAP¶

In [115]:
import plotly.express as px

# Create a treemap (assuming hierarchical data, use 'batter' and 'batsman_runs')
fig = px.treemap(
    top10_df,
    path=['batter'],          # Path for hierarchy (just batter here)
    values='batsman_runs',    # Values for sizing
    color='batsman_runs',     # Color by runs
    color_continuous_scale='RdYlGn'  # Color scale
)

fig.update_layout(title='Treemap of Top 10 IPL Batsmen Runs')
fig.show()

BOX PLOT¶

In [ ]:
trace = go.Box(
    y=top10_df['batsman_runs'],
    boxpoints='all',  # Show all points
    jitter=0.3,       # Spread the points
    pointpos=-1.8,    # Offset points to the left
    marker_color='blue'
)

layout = go.Layout(
    title='Box Plot of Batsman Runs',
    yaxis={'title': 'Batsman Runs'}
)

fig = go.Figure(data=[trace], layout=layout)
pyo.plot(fig)

3D Surface Plot¶

In [116]:
# Example with hypothetical data
z_data = np.random.rand(10, 10)

trace = go.Surface(z=z_data) # creates a 3D surface plot
layout = go.Layout(
    title='3D Surface Plot of IPL Data',
    scene = dict(                      #defines the 3D plot’s axes:
        xaxis_title='Batter',
        yaxis_title='Matches/Seasons',
        zaxis_title='Runs'
    )
)

fig = go.Figure(data=[trace], layout=layout)
pyo.iplot(fig)

z_data is a 10x10 array of random numbers between 0 and 1. This data represents the z values (the height values in the 3D plot)¶

Funnel Chart¶

In [ ]:
import plotly.express as px

# Create a funnel chart to show progressive runs by batsmen
fig = px.funnel(
    top10_df,
    x='batsman_runs',  # Values for the funnel stages
    y='batter'         # Names for the funnel stages
)

fig.update_layout(title='Funnel Chart of Batsman Runs')
fig.show()
In [ ]:
pyo.plot(fig)

Waterfall Chart¶

A waterfall chart shows the cumulative effect of sequentially introduced positive or negative values¶

In [86]:
import plotly.graph_objects as go

trace = go.Waterfall(
    x=top10_df['batter'],
    y=top10_df['batsman_runs'],
    connector={"line":{"color":"rgb(63, 63, 63)"}}             
)

layout = go.Layout(
    title='Waterfall Chart of Batsman Runs',
    xaxis={'title': 'Batter'},
    yaxis={'title': 'Cumulative Runs'}
)

fig = go.Figure(data=[trace], layout=layout)
In [87]:
pyo.plot(fig)
Out[87]:
'temp-plot.html'
In [ ]:
fig.show()

Sunburst Chart¶

Sunburst charts are used to represent hierarchical data.¶

In [117]:
# Create a sunburst chart with the correct column names
fig = px.sunburst(
    top10_df,
    path=['batting_team', 'batter'],  # Create hierarchy (team -> batter)
    values='batsman_runs',  # Values to be represented in the chart
    color='batsman_runs',  # Color by number of runs
    color_continuous_scale='RdBu'
)

fig.update_layout(title='Sunburst Chart of Batsman Runs by Team')
fig.show()
In [81]:
pyo.plot(fig)
Out[81]:
'temp-plot.html'

Violin Plot¶

A violin plot is useful to visualize the distribution of the data and its probability density.¶

In [ ]:
import plotly.express as px

# Create a violin plot for batsman runs distribution
fig = px.violin(
    top10_df,
    y='batsman_runs',
    box=True,  # Add a box plot inside the violin
    points='all'  # Show all points
)

fig.update_layout(title='Violin Plot of Batsman Runs')
fig.show()
In [ ]:
pyo.plot(fig)

update_layout: This method allows you to update the layout of the figure, including aspects such as the title, axis labels, colors, and more.¶

Polar Chart¶

Polar charts are useful for visualizing data in circular layouts¶

In [82]:
import plotly.express as px

# Create a polar chart
fig = px.line_polar(
    top10_df,
    r='batsman_runs',  # Radial axis (runs)
    theta='batter',    # Angular axis (batsmen)
    line_close=True,   # Close the line to form a loop
    template="plotly_dark"
)

fig.update_layout(title='Polar Chart of Top 10 IPL Batsmen by Runs')
fig.show()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_28536\864864616.py in <module>
      2 
      3 # Create a polar chart
----> 4 fig = px.line_polar(
      5     top10_df,
      6     r='batsman_runs',  # Radial axis (runs)

~\anaconda3\lib\site-packages\plotly\express\_chart_types.py in line_polar(data_frame, r, theta, color, line_dash, hover_name, hover_data, custom_data, line_group, text, symbol, animation_frame, animation_group, category_orders, labels, color_discrete_sequence, color_discrete_map, line_dash_sequence, line_dash_map, symbol_sequence, symbol_map, markers, direction, start_angle, line_close, line_shape, render_mode, range_r, range_theta, log_r, title, template, width, height)
    996     of a polyline mark in polar coordinates.
    997     """
--> 998     return make_figure(args=locals(), constructor=go.Scatterpolar)
    999 
   1000 

~\anaconda3\lib\site-packages\plotly\express\_core.py in make_figure(args, constructor, trace_patch, layout_patch)
   2298                 args, trace_spec, group, mapping_labels.copy(), sizeref
   2299             )
-> 2300             trace.update(patch)
   2301             if fit_results is not None:
   2302                 trendline_rows.append(mapping_labels.copy())

~\anaconda3\lib\site-packages\plotly\basedatatypes.py in update(self, dict1, overwrite, **kwargs)
   5131                 BaseFigure._perform_update(self, kwargs, overwrite=overwrite)
   5132         else:
-> 5133             BaseFigure._perform_update(self, dict1, overwrite=overwrite)
   5134             BaseFigure._perform_update(self, kwargs, overwrite=overwrite)
   5135 

~\anaconda3\lib\site-packages\plotly\basedatatypes.py in _perform_update(plotly_obj, update_obj, overwrite)
   3911                     # Update compound objects recursively
   3912                     # plotly_obj[key].update(val)
-> 3913                     BaseFigure._perform_update(plotly_obj[key], val)
   3914                 elif isinstance(validator, CompoundArrayValidator):
   3915                     if plotly_obj[key]:

~\anaconda3\lib\site-packages\plotly\basedatatypes.py in _perform_update(plotly_obj, update_obj, overwrite)
   3888                 err = _check_path_in_prop_tree(plotly_obj, key, error_cast=ValueError)
   3889                 if err is not None:
-> 3890                     raise err
   3891 
   3892             # Convert update_obj to dict

ValueError: Invalid property specified for object of type plotly.graph_objs.scatterpolargl.Line: 'shape'

Did you mean "dash"?

    Valid properties:
        color
            Sets the line color.
        dash
            Sets the style of the lines.
        width
            Sets the line width (in px).
        
Did you mean "dash"?

Bad property path:
shape
^^^^^

Donut Chart (Pie Chart Variation)¶

Donut charts are variations of pie charts where the center is hollow, allowing we can add an additional layer of information.¶

In [118]:
# Create a donut chart to represent the percentage of total runs by each batter
fig = px.pie(
    top10_df, 
    names='batter', 
    values='batsman_runs', 
    hole=0.4  # Hollow center to create a donut chart
)

fig.update_layout(title='Donut Chart of Top 10 IPL Batsmen by Runs')
In [ ]:
pyo.plot(fig)

Density Plot¶

A density plot (or Kernel Density Estimate) provides a smoothed estimate of the distribution.¶

In [119]:
# Create a density plot
fig = px.density_contour(
    top10_df,
    x='batsman_runs',  # Variable for the x-axis
    title='Density Plot of Batsman Runs'
)

fig.update_layout(
    xaxis_title='Batsman Runs',
    yaxis_title='Density'
)

fig.show()
In [85]:
pyo.plot(fig)
Out[85]:
'temp-plot.html'

HISTOGRAM¶

In [120]:
# Create a histogram
fig = px.histogram(
    top10_df,
    x='batsman_runs',  # Variable for the x-axis
    nbins=20,          # Number of bins
    title='Histogram of Batsman Runs'
)

fig.update_layout(
    xaxis_title='Batsman Runs',
    yaxis_title='Frequency'
)

fig.show()
In [71]:
pyo.plot(fig)
Out[71]:
'temp-plot.html'

Combined Histogram and KDE Plot¶

In [123]:
import plotly.graph_objects as go
import plotly.subplots as sp

# Create subplots
fig = sp.make_subplots(rows=1, cols=2, subplot_titles=('Histogram', 'KDE'))

# Add Histogram
histogram = go.Histogram(
    x=top10_df['batsman_runs'],
    nbinsx=20,
    name='Histogram'
)
fig.add_trace(histogram, row=1, col=1)

# Add KDE
kde = go.Histogram(
    x=top10_df['batsman_runs'],
    histnorm='density',
    name='KDE'
)
fig.add_trace(kde, row=1, col=2)

fig.update_layout(
    title='Histogram and KDE of Batsman Runs',
    xaxis_title='Batsman Runs',
    yaxis_title='Frequency',
    xaxis2_title='Batsman Runs',
    yaxis2_title='Density'
)

fig.show()

Overlay Multiple Histograms¶

In [125]:
# Sample data for overlay
top10_df2 = top10_df.copy()
top10_df2['batsman_runs'] = top10_df2['batsman_runs'] + 5  # Modify data for demonstration

fig = go.Figure()

fig.add_trace(go.Histogram(
    x=top10_df['batsman_runs'],
    nbinsx=30,
    opacity=0.6,
    name='Dataset 1'
))

fig.add_trace(go.Histogram(
    x=top10_df2['batsman_runs'],
    nbinsx=30,
    opacity=0.6,
    name='Dataset 2'
))

fig.update_layout(
    title='Overlay of Multiple Histograms',
    xaxis_title='Batsman Runs',
    yaxis_title='Frequency',
    barmode='overlay'  # Overlay bars
)

fig.show()

STACKED HISTOGRAM¶

In [126]:
# Sample data for stacked histograms
fig = go.Figure()

fig.add_trace(go.Histogram(
    x=top10_df['batsman_runs'],
    nbinsx=30,
    name='Dataset 1',
    opacity=0.7
))

fig.add_trace(go.Histogram(
    x=top10_df2['batsman_runs'],
    nbinsx=30,
    name='Dataset 2',
    opacity=0.7
))

fig.update_layout(
    title='Stacked Histograms of Batsman Runs',
    xaxis_title='Batsman Runs',
    yaxis_title='Frequency',
    barmode='stack'  # Stack bars on top of each other
)

fig.show()

Histogram with Hover Data¶

In [127]:
fig = go.Figure()

fig.add_trace(go.Histogram(
    x=top10_df['batsman_runs'],
    nbinsx=30,
    name='Runs Distribution',
    hoverinfo='x+y'  # Show x and y values on hover
))

fig.update_layout(
    title='Histogram with Hover Data',
    xaxis_title='Batsman Runs',
    yaxis_title='Frequency'
)

fig.show()

BOX PLOT WITH CUSTOMIZED WHISKERS¶

In [129]:
# Box Plot with customized whiskers
fig = go.Figure()

fig.add_trace(go.Box(
    y=top10_df['batsman_runs'],
    name='Batsman Runs',
    boxmean='sd',
    whiskerwidth=0.4,  # Width of the whiskers
    line_color='rgba(255, 100, 102, 0.8)'
))

fig.update_layout(
    title='Box Plot with Customized Whiskers',
    yaxis_title='Batsman Runs'
)

fig.show()
In [ ]: